A Poorly Known High-Latitude Parasitoid Wasp Community: Unexpected Diversity and Dramatic Changes through Time

Climate change will have profound and unanticipated effects on species distributions. The pace and nature of this change is largely unstudied, especially for the most diverse elements of terrestrial communities – the arthropods – here we have only limited knowledge concerning the taxonomy and the ecology of these groups. Because Arctic ecosystems have already experienced significant increases in temperature over the past half century, shifts in community structure may already be in progress. Here we utilise collections of a particularly hyperdiverse insect group – parasitoid wasps (Hymenoptera; Braconidae; Microgastrinae) – at Churchill, Manitoba, Canada in the early and mid-twentieth century to compare the composition of the contemporary community to that present 50–70 years ago. Morphological and DNA barcoding results revealed the presence of 79 species of microgastrine wasps in collections from Churchill, but we estimate that 20% of the local fauna awaits detection. Species composition and diversity between the two time periods differ significantly; species that were most common in historic collections were not found in contemporary collections and vice versa. Using barcodes we compared these collections to others from across North America; contemporary Churchill species are most affiliated with more south-western collections, while historic collections were more affiliated with eastern collections. The past five decades has clearly seen a dramatic change of species composition within the area studied coincident with rising temperature

Multiple Oncogenic Pathway Signatures Show Coordinate Expression Patterns in Human Prostate Tumors

BACKGROUND: Gene transcription patterns associated with activation of oncogenes Myc, c-Src, beta-catenin, E2F3, H-Ras, HER2, EGFR, MEK, Raf, MAPK, Akt, and cyclin D1, as well as of the cell cycle and of androgen signaling have been generated in previous studies using experimental models. It was not clear whether genes in these "oncogenic signatures" would show coordinate expression patterns in human prostate tumors, particularly as most of the signatures were derived from cell types other than prostate. PRINCIPAL FINDINGS: The above oncogenic pathway signatures were examined in four different gene expression profile datasets of human prostate tumors (representing approximately 250 patients in all), using both Q1-Q2 and one-sided Fisher's exact enrichment analysis methods. A significant fraction (approximately 5%) of genes up-regulated experimentally by Myc, c-Src, HER2, Akt, or androgen were co-expressed in human tumors with the oncogene or biomarker corresponding to the pathway signature. Genes down-regulated experimentally, however, did not show anticipated patterns of anti-enrichment in the human tumors. CONCLUSIONS: Significant subsets of the genes in these experimentally-derived oncogenic signatures are relevant to the study of human prostate cancer. Both molecular biologists and clinical researchers could focus attention on the relatively small number of genes identified here as having coordinate patterns that arise from both the experimental system and the human disease system

An integration of complementary strategies for gene-expression analysis to reveal novel therapeutic opportunities for breast cancer

INTRODUCTION. Perhaps the major challenge in developing more effective therapeutic strategies for the treatment of breast cancer patients is confronting the heterogeneity of the disease, recognizing that breast cancer is not one disease but multiple disorders with distinct underlying mechanisms. Gene-expression profiling studies have been used to dissect this complexity, and our previous studies identified a series of intrinsic subtypes of breast cancer that define distinct populations of patients with respect to survival. Additional work has also used signatures of oncogenic pathway deregulation to dissect breast cancer heterogeneity as well as to suggest therapeutic opportunities linked to pathway activation. METHODS. We used genomic analyses to identify relations between breast cancer subtypes, pathway deregulation, and drug sensitivity. For these studies, we use three independent breast cancer gene-expression data sets to measure an individual tumor phenotype. Correlation between pathway status and subtype are examined and linked to predictions for response to conventional chemotherapies. RESULTS. We reveal patterns of pathway activation characteristic of each molecular breast cancer subtype, including within the more aggressive subtypes in which novel therapeutic opportunities are critically needed. Whereas some oncogenic pathways have high correlations to breast cancer subtype (RAS, CTNNB1, p53, HER1), others have high variability of activity within a specific subtype (MYC, E2F3, SRC), reflecting biology independent of common clinical factors. Additionally, we combined these analyses with predictions of sensitivity to commonly used cytotoxic chemotherapies to provide additional opportunities for therapeutics specific to the intrinsic subtype that might be better aligned with the characteristics of the individual patient. CONCLUSIONS. Genomic analyses can be used to dissect the heterogeneity of breast cancer. We use an integrated analysis of breast cancer that combines independent methods of genomic analyses to highlight the complexity of signaling pathways underlying different breast cancer phenotypes and to identify optimal therapeutic opportunities.V Foundation for Cancer Research (Partners in Excellence grant

A systematic, large-scale comparison of transcription factor binding site models

Background The modelling of gene regulation is a major challenge in biomedical research. This process is dominated by transcription factors (TFs) and mutations in their binding sites (TFBSs) may cause the misregulation of genes, eventually leading to disease. The consequences of DNA variants on TF binding are modelled in silico using binding matrices, but it remains unclear whether these are capable of accurately representing in vivo binding. In this study, we present a systematic comparison of binding models for 82 human TFs from three freely available sources: JASPAR matrices, HT-SELEX-generated models and matrices derived from protein binding microarrays (PBMs). We determined their ability to detect experimentally verified “real” in vivo TFBSs derived from ENCODE ChIP-seq data. As negative controls we chose random downstream exonic sequences, which are unlikely to harbour TFBS. All models were assessed by receiver operating characteristics (ROC) analysis. Results While the area- under-curve was low for most of the tested models with only 47 % reaching a score of 0.7 or higher, we noticed strong differences between the various position-specific scoring matrices with JASPAR and HT-SELEX models showing higher success rates than PBM-derived models. In addition, we found that while TFBS sequences showed a higher degree of conservation than randomly chosen sequences, there was a high variability between individual TFBSs. Conclusions Our results show that only few of the matrix-based models used to predict potential TFBS are able to reliably detect experimentally confirmed TFBS. We compiled our findings in a freely accessible web application called ePOSSUM (http:/mutationtaster.charite.de/ePOSSUM/) which uses a Bayes classifier to assess the impact of genetic alterations on TF binding in user-defined sequences. Additionally, ePOSSUM provides information on the reliability of the prediction using our test set of experimentally confirmed binding sites

Functional Characterization of Transcription Factor Motifs Using Cross-species Comparison across Large Evolutionary Distances

We address the problem of finding statistically significant associations between cis-regulatory motifs and functional gene sets, in order to understand the biological roles of transcription factors. We develop a computational framework for this task, whose features include a new statistical score for motif scanning, the use of different scores for predicting targets of different motifs, and new ways to deal with redundancies among significant motif–function associations. This framework is applied to the recently sequenced genome of the jewel wasp, Nasonia vitripennis, making use of the existing knowledge of motifs and gene annotations in another insect genome, that of the fruitfly. The framework uses cross-species comparison to improve the specificity of its predictions, and does so without relying upon non-coding sequence alignment. It is therefore well suited for comparative genomics across large evolutionary divergences, where existing alignment-based methods are not applicable. We also apply the framework to find motifs associated with socially regulated gene sets in the honeybee, Apis mellifera, using comparisons with Nasonia, a solitary species, to identify honeybee-specific associations

Effects of C282Y, H63D, and S65C HFE gene mutations, diet, and life-style factors on iron status in a general Mediterranean population from Tarragona, Spain

Mutations in the HFE gene result in iron overload and can produce hereditary hemochromatosis (HH), a disorder of iron metabolism characterized by increased intestinal iron absorption. Dietary quality, alcoholism and other life-style factors can increase the risk of iron overload, especially among genetically at risk populations. Polymorphisms of the HFE gene (C282Y, H63D and S65C) were measured together with serum ferritin (SF), transferrin saturation (TS) and hemoglobin, to measure iron status, in randomly-selected healthy subjects living in the Spanish Mediterranean coast (n = 815; 425 females, 390 males), 18 to 75 years of age. The intake of dietary components that affect iron absorption was calculated from 3-day dietary records. The presence of C282Y/H63D compound heterozygote that had a prevalence of 2.8% in males and 1.2% in females was associated with an elevated TS and SF. No subject was homozygous for C282Y or S65C. The C282Y heterozygote, H63D heterozygote and homozygote and H63D/S65C compound heterozygote genotypes were associated with increased TS relative to the wild type in the general population. These genotypes together with the alcohol and iron intake increase the indicators of iron status, while calcium intake decreases them. We did not observe any affect of the S65C heterozygote genotype on these levels. All the HFE genotypes except for the S65C heterozygote together with the alcohol, iron and calcium intake affect the indicators of iron status. The C282Y/H63D compound heterozygote genotype has the higher phenotypic expression in our Spanish Mediterranean population

Mining and state-space modeling and verification of sub-networks from large-scale biomolecular networks

<p>Abstract</p> <p>Background</p> <p>Biomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.</p> <p>Results</p> <p>In this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent <it>hypotheses </it>that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.</p> <p>Conclusion</p> <p>Experimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.</p